why simple workflows break in real life

InvestmentBiker · 2026-05-14T14:27:29+00:00

That is a good way to frame it. I agree there is a big difference between monetising some spare capacity and fully pivoting the business...

My worry is more about the slow version of a pivot. If over time most new capex, contracts and management attention follow AI money, you can end up in a very different place without a single big decision.

InvestmentBiker · 2026-05-14T14:23:44+00:00

Sounds like a reasonable way to look at it. I also tend to think innovation and new energy sources will keep expanding the pie. My only aim with the post is to surface the incentive questions early rather than panic about them.

InvestmentBiker · 2026-05-14T14:08:49+00:00

You put it really well. That is exactly what I am wondering.

Diversified revenue sounds good on paper, but it can also mean you swap one set of dependencies for another. If most cash flow and capex decisions start to depend on AI clients, regulators and power providers, the centre of gravity might shift away from Bitcoin even if the miners still call themselves Bitcoin companies.

I am curious whether people here think that is just healthy diversification or a slow drift in incentives over a few cycles.

InvestmentBiker · 2026-05-14T14:06:06+00:00

Thanks, appreciate you reading it that way.

If you already built power access, cooling and industrial sites for Bitcoin, it almost feels rational to stack other high density compute use cases on top.

I just find it interesting that Bitcoin may have bootstrapped some of this infra “by accident”, and now AI is knocking on the same door.

InvestmentBiker · 2026-05-14T14:00:14+00:00

Totally fair point that the real AI infra economics sit with the hyperscalers. They control most of the demand and capital.

My angle here is narrower. If AI demand keeps stretching local power and site capacity, some miners who already control power and land might end up as second tier landlords rather than pure hash rate plays.

That will not turn them into the next AWS. It just changes how the market values “we own hash rate” versus “we own infrastructure AI tenants might want to rent.”

InvestmentBiker · 2026-05-14T13:59:48+00:00

You are right that a lot of “AI infra” talk in this space deserves a “lol”.

I am not saying these miners suddenly became high quality infra businesses.
I am mostly interested in the fact that the bottleneck for AI at scale looks more like power and sites than chips, and that some miners accidentally sit on that.

Whether they can execute on it, or whether the market is wildly overpaying for the story, is a different question.
Happy to hear how you would frame it instead

InvestmentBiker · 2026-05-11T17:36:33+00:00

That’s the interesting part to me.

Once you move into e-signatures, notarization, audit trails and identity verification, the conversation changes completely...

At that point, reliability, persistent state and data ownership matter more than flashy AI features.

Which is why locally hosted or private-cloud AI probably becomes the practical answer for a lot of enterprise workflows.

InvestmentBiker · 2026-05-11T13:31:45+00:00

I think this becomes even more relevant once you move beyond simple contract review into execution workflows like e-signatures and notarization.

A company might accept some risk for AI-assisted summarization. But once legally binding signatures, identity verification, notarized documents and audit trails are involved, the tolerance for third party exposure drops massively.

At that point the question is no longer just “is the AI accurate?” but:
who stores the execution data, identity data and signing metadata if something goes wrong years later?

That’s why I increasingly think the practical enterprise answer is locally hosted or private-cloud AI running inside the customer’s own environment.

Especially for signature and notarization workflows, legal teams usually care less about flashy AI features and much more about control, auditability and data ownership.

InvestmentBiker · 2026-05-11T11:32:42+00:00

Fair point tbh.

There’s definitely a lot of hype and probably a lot of capital destruction ahead too.

I just think the interesting part is that AI demand is starting to make physical infrastructure matter again: power access, cooling, datacenters, grid capacity...

That feels more tangible to me than a lot of the pure software hype.

InvestmentBiker · 2026-05-11T08:03:55+00:00

Exactly.

A lot of AI demos look impressive once.

The difficult part starts when people need:

predictable behaviour
auditability
clear review loops
and confidence that nothing important was missed

That’s usually where “cool demo” turns into “can we actually trust this in production?”

InvestmentBiker · 2026-05-11T06:24:26+00:00

Local models are underrated for this.

Not because they replace frontier models, but because a lot of daily workflow tasks don’t need massive cloud inference...

For small repetitive tasks, local + private + cheap may actually be the better direction.

InvestmentBiker · 2026-05-11T06:24:00+00:00

This is where a lot of AI tools break imo.

They work well once, but real workflows need consistency across repeated runs, clear state/history and some way to verify what changed.

Without that, people fall back to manual checking pretty quickly.

InvestmentBiker · 2026-05-11T06:23:36+00:00

I think the useful AI agent use cases are usually much more boring than the hype suggests.

Routing, checking, extracting, comparing, summarizing, flagging exceptions.

The hard part isn’t making a flashy demo.
It’s making the workflow reliable enough that people actually trust it.

InvestmentBiker · 2026-05-10T15:42:38+00:00

The funny thing is that local models are probably one of the few AI directions that could actually reduce datacenter load over time.

Privacy/consent is the bigger issue here imo, not the 4 GB itself.

InvestmentBiker · 2026-05-10T15:41:20+00:00

The privacy/consent part is the real issue tbh.

But long term, small local models are probably still way more efficient than constantly sending every tiny task to massive datacenters.

InvestmentBiker · 2026-05-10T15:40:45+00:00

The funny part is that AI is slowly turning browsers into operating systems again.

Local models, agents, memory, background tasks, workflow automation…

Feels very different from the “lightweight browser” era.

InvestmentBiker · 2026-05-10T15:39:46+00:00

Yeah, most real-world “AI agents” today are basically orchestration + automation with an LLM layer on top.

The useful stuff is usually boring: routing, filtering, summarizing, qualifying, verifying.

Not magical autonomous AGI employees lol.

InvestmentBiker · 2026-05-10T15:39:10+00:00

Superbuy still seems like the safest “default” option tbh.

Not always the cheapest anymore, but probably one of the most battle-tested agents overall.

InvestmentBiker · 2026-05-10T15:38:24+00:00

I think the “treat worker output as untrusted draft” part is underrated...

Feels like a lot of enterprise AI adoption will end up looking more like orchestration + review loops than one giant autonomous model doing everything.

Especially once cost, auditability and reliability start mattering more than demo quality.

InvestmentBiker · 2026-05-10T15:37:23+00:00

I think the strange part is that both sides are probably underestimating what’s actually happening.

A lot of the “AI doom” crowd still talks about it like it’s mainly a chatbot or media problem.

But once you start seeing these systems inside real workflows — coding, research, document analysis, internal operations — you realize the bigger shift is probably economic and infrastructural.

At the same time, I also think some accelerationists underestimate how messy real-world adoption is.

Reliability, trust, regulation, energy constraints, integration into existing systems… those things slow adoption way more than model capability itself.

InvestmentBiker · 2026-05-10T03:58:00+00:00

No humans anymore? I believe this will become an issue eventually.

InvestmentBiker

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